Ensemble of rough-neuro-fuzzy systems for classification with missing features

Most methods constituting the soft computing concept can not handle data with missing or unknown features. Neural networks are able to perfectly fit to data and fuzzy logic systems use interpretable knowledge. To achieve better accuracy learning systems can be combined into larger ensembles. In this paper we combine logical neuro-fuzzy systems into the AdaBoost ensemble and extract fuzzy rules from the ensemble. The rules are used in rough-neuro-fuzzy classifier which can operate on data with missing values. The rough systems perform very well on these rules which was illustrated on a well known benchmark. The features were being removed to check the performance on incomplete data sets.

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